Systems for identifying persons through intrinsic human traits have been developed. These systems operate by taking images of a physiological trait of a person and comparing information stored in the image to image data corresponding to the imaged trait for a particular person. When the information stored in the image has a high degree of correlation to the relevant data previously obtained for a particular person's trait, positive identification of the person may be obtained. These biometric systems obtain and compare data for physical features, such as fingerprints, voice, facial characteristics, iris patterns, hand geometry, retina patterns, and hand/palm vein structure. Different traits impose different constraints on the identification processes of these systems. For example, fingerprint recognition systems require the person to be identified to contact an object directly for the purpose of obtaining fingerprint data from the object. Similarly, retina pattern identification systems require a person to allow an imaging system to scan the retinal pattern within one's eye for an image capture of the pattern that identifies a person. Facial feature recognition systems, however, do not require direct contact with a person and these biometric systems are capable of capturing identification data without the cooperation of the person to be identified.
One trait especially suited for identification is sclera patterns in a person's eye. The human eye sclera provides a unique trait that changes little over a person's lifetime. It also provide multi-layer information that can be used for liveness test. It is important to design a method to segment and match the sclera pattern accurately and robustly.